Search for dissertations about: "Compressed Sensing"
Showing result 1 - 5 of 22 swedish dissertations containing the words Compressed Sensing.
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1. Compressed Sensing : Algorithms and Applications
Abstract : The theoretical problem of finding the solution to an underdeterminedset of linear equations has for several years attracted considerable attentionin the literature. This problem has many practical applications.One example of such an application is compressed sensing (cs), whichhas the potential to revolutionize how we acquire and process signals. READ MORE
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2. Liquid Sensing : Development and Characterisation of an Electronic Tongue Based on Electrochemical Methods
Abstract : A new sensor technology for liquid sensing is reported, i.e. the electronic tongue based on electrochemical methods. Such a system involves the combination of non-selective sensors (metal electrodes) and a signal processing part. READ MORE
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3. Source and Channel Coding for Compressed Sensing and Control
Abstract : Rapid advances in sensor technologies have fueled massive torrents of data streaming across networks. Such large volume of information, indeed, restricts the operational performance of data processing, causing inefficiency in sensing, computation, communication and control. READ MORE
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4. Non-Convex Methods for Compressed Sensing and Low-Rank Matrix Problems
Abstract : In this thesis we study functionals of the type \( \mathcal{K}_{f,A,\b}(\x)= \mathcal{Q}(f)(\x) + \|A\x - \b \| ^2 \), where \(A\) is a linear map, \(\b\) a measurements vector and \( \mathcal{Q} \) is a functional transform called \emph{quadratic envelope}; this object is a very close relative of the \emph{Lasry-Lions envelope} and its use is meant to regularize the functionals \(f\). Carlsson and Olsson investigated in earlier works the connections between the functionals \( \mathcal{K}_{f,A,\b}\) and their unregularized counterparts \(f(\x) + \|A\x - \b \| ^2 \). READ MORE
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5. Greedy Algorithms for Distributed Compressed Sensing
Abstract : Compressed sensing (CS) is a recently invented sub-sampling technique that utilizes sparsity in full signals. Most natural signals possess this sparsity property. From a sub-sampled vector, some CS reconstruction algorithm is used to recover the full signal. READ MORE